Geology topics

Erich H. Peitzsch

Publications and source records attributed to Erich H. Peitzsch.

At least 19 recordsLinked to original sources

USGS2026

Changing drivers of regional large magnitude avalanche frequency throughout Colorado, USA

Large magnitude snow avalanches (destructive size ≥ D3) impact settlements, transportation corridors, and public safety worldwide. In Colorado, United States, avalanches have killed more people than any other natural hazard since 1950. In March 2019, a large magnitude avalanche cycle occurred throughout the entire mountainous portion of Colorado resulting in more than 1000 reported avalanches during a two-week period. Nearly 200 of these avalanches were size D4 or larger with at least three D5 avalanches. However, placing this 2019 large magnitude avalanche cycle in historic context requires data prior to the instrumental record. Here, we paired tree disturbance data from dendrochronology (1698 to 2020) with meteorological data from the modeled and instrumental record (1901 to 2020) to understand the frequency and climate drivers of large magnitude snow avalanche cycles. The extensive number of downed trees from the 2019 avalanche cycle allowed us to collect 1,188 cross-sections and cores from 1023 individual trees within 24 avalanche paths across the state. From these samples we identified 4135 avalanche-related growth disturbances. We employed a strategic nested sampling design to spatially aggregate avalanche frequency from individual avalanche paths, to counties, to three major sub-regions (i.e., north, central, and south), and across the entire region (i.e., state of Colorado). Over a period spanning more than three centuries (1698 to 2020), we identified 76 avalanche years within 24 individual avalanche paths. Large magnitude avalanche event frequency varied across paths and sub-regions with several notable region-wide avalanche cycles. Both tree-ring and historical written records highlighted 1899 as a year with widespread and large magnitude avalanche activity similar to the March 2019 avalanche cycle. Since the early-20th century (1900 to 2020) regional avalanche probability declined significantly in parallel with decreasing snowpack throughout Colorado. Similarly, dominant avalanche regimes shifted from large magnitude regional cycles driven by above average snowfall years over most of the record, to regional avalanche cycles occurring more commonly in average to low snow years since 1988. In recent decades, a lack of December precipitation and above average March precipitation characterized years with regional large magnitude avalanche activity. Even with declining snow water equivalent, truly extreme regional large magnitude avalanche cycles remain possible – as demonstrated by the 2019 cycle. This underscores that rare but high-impact events are not eliminated by long-term trends. Understanding the changing snow and weather drivers and subsequent behavior of large magnitude avalanche cycles across multiple spatial scales may improve avalanche forecasting and the products and mitigations strategies developed by structural engineers to mitigate avalanche danger. This can decrease the avalanche risk to the public and improve infrastructure design in avalanche terrain.

Colorado↗

USGS2025

Documenting, quantifying, and modeling a large glide avalanche in Glacier National Park, Montana, USA

Glide avalanches present a significant and repetitive challenge to many operational forecasting programs, and they are likely to become more frequent. While the spatial location of glide release areas is extremely consistent, the onset of glide avalanche release is notoriously difficult to forecast, and their destructive potential can be immense. Thus, the timing and dynamics of glide avalanches is an important area of study. To better understand these processes, and to improve assessments of risk to transportation corridors and infrastructure, event documentation is key. Here, we survey a large glide avalanche event along the Going-to-the-Sun Road in Glacier National Park, Montana, USA, during road opening operations in the spring of 2022. Using three sets of terrestrial lidar data (pre-event, post-event, and snow-off), we quantified key aspects of the avalanche and created powerful visualizations for analysis. Further, we evaluated meteorological data from automated weather stations between the onset of glide cracking and avalanche release. Last, we synthesized lidar data with a numerical dynamics model to replicate the event in a simulated environment. Using the tuned model, we determined the critical mean snow depth in the release area necessary for an avalanche to reach the road (4.2 m). Our method may be of particular use for glide avalanches, which tend to release in roughly the same place and time each year at a known interface. This could make the calculated critical depths more consistently reliable and preclude the need for additional tuning in dynamics models. As 1) lidar technology continues to improve and reduce in cost, 2) transportation corridors continue to extend into avalanche terrain, and 3) glide avalanches potentially become increasingly frequent, the synthesis outlined here provides a valuable tool for operational forecasters considering infrastructure threatened by glide events.

Montana↗

USGS2025

Mountain sentinels in a changing world: Review and conservation implications of weather and climate effects on mountain goats (Oreamnos americanus)

Climate change is occurring at an accelerated rate in high-elevation alpine and mountain ecosystems. Cold-adapted, mountain species are at risk due to forecasted change and knowledge is needed to respond to current and future conservation challenges. Mountain goats ( Oreamnos americanus ) are an iconic species of North American mountain cultures and landscapes, and due to specialized adaptations for life in cold, mountainous environments they are particularly sensitive to changes in weather and climate. As sentinels of change in alpine ecosystems, the study of mountain goats offers insight into the ecological effects and conservation challenges associated with climate change in these sensitive and biodiverse environments. Here, we synthesize existing knowledge about how climate change is expected to influence environmental conditions experienced by mountain goats and associated mechanistic changes to behavior, nutritional ecology, demography, health, and interspecific interactions. In many instances, climate change effects are likely to be negative and additive to existing threats (such as human disturbance, hunting, disease, predation) though benefits are expected in some cases. Changes in climate and mountain environments will necessitate re-examination and modification of population monitoring, management, and conservation strategies. Specifically, spatiotemporal (and other) aspects of monitoring and management may need to be adjusted to accommodate emerging and novel conservation challenges. Yet, key data and knowledge gaps remain and should be addressed to advance conservation and decision-making capabilities. For mountain goats and similarly climate-sensitive alpine herbivores, effective conservation will ultimately benefit from collaborations among diverse networks guided by well-planned, strategic visions focused on common ground – namely the resiliency and persistence of culturally and ecologically significant mountain species and the alpine environment they inhabit.

Alaska, Alberta, British Columbia, Idaho, Montana,↗

USGS2024

Cryospheric sciences at the U.S. Geological Survey

Introduction The cryosphere is the collective parts of the Earth where water is in its frozen state and includes snow, glaciers, ice sheets, ice shelves, freshwater ice, sea ice, and permafrost. The cryosphere is a climate indicator and climate regulator. Surface cryosphere features, such as glaciers, snow, and sea ice, store freshwater and make the surface of the Earth bright white, which affects how much energy from the sun is absorbed or reflected by the planet. The subsurface cryosphere, such as permafrost and ground ice, stores water and other materials, including carbon, nutrients, heavy metals, and viruses. Carbon storage in permafrost is one way the cryosphere regulates the Earth's climate. Changes to the cryosphere indicate the state of the Earth's climate. The cryosphere spans many regions of the Earth, from subtropical regions of the Himalayas to polar regions of the Arctic and Antarctica, and intersects many of the Earth's spheres, including the lithosphere, biosphere, atmosphere, and hydrosphere.

Fact Sheet↗

USGS2024

Snow avalanches are a primary climate-linked driver of mountain ungulate populations

Snow is a major, climate-sensitive feature of the Earth’s surface and catalyst of fundamentally important ecosystem processes. Understanding how snow influences sentinel species in rapidly changing mountain ecosystems is particularly critical. Whereas effects of snow on food availability, energy expenditure, and predation are well documented, we report how avalanches exert major impacts on an ecologically significant mountain ungulate - the coastal Alaskan mountain goat ( Oreamnos americanus ). Using long-term GPS data and field observations across four populations (421 individuals over 17 years), we show that avalanches caused 23−65% of all mortality, depending on area. Deaths varied seasonally and were directly linked to spatial movement patterns and avalanche terrain use. Population-level avalanche mortality, 61% of which comprised reproductively important prime-aged individuals, averaged 8% annually and exceeded 22% when avalanche conditions were severe. Our findings reveal a widespread but previously undescribed pathway by which snow can elicit major population-level impacts and shape demographic characteristics of slow-growing populations of mountain-adapted animals.

Alaska↗

USGS2024

Characterizing vegetation and return periods in avalanche paths using lidar and aerial imagery

Snow avalanches are a hazard and ecological disturbance across mountain landscapes worldwide. Understanding how avalanche frequency affects forests and vegetation improves infrastructure planning, risk management, and avalanche forecasting. We implemented a novel approach using lidar, aerial imagery, and a random forest model to classify imagery-observed vegetation within avalanche paths in southern Glacier National Park, Montana, USA. We calculated spatially explicit avalanche return periods using a physically based spatial interpolation method and characterized the vegetation within those return period zones. The automated vegetation classification model differed slightly between avalanche paths, but the combination of lidar and spectral signature metrics provided the best accuracy (88–92 percent) for predicting vegetation classes within complex avalanche terrain rather than lidar or spectral signature metrics alone. The highest frequency avalanche return periods were broadly characterized by grassland and shrubland, but the influence of topography greatly influences the vegetation classes as well as the return periods. Furthermore, statistically significant differences in lidar-derived vegetation canopy height exist between categorical return periods. The ability to characterize vegetation within various avalanche return periods using remote sensing data provides land use planners and avalanche forecasters a tool for assessing the spatial extent of large-magnitude avalanches in individual avalanche paths.

Montana↗

USGS2024

Detecting avalanche path ground cover and vegetation change across multiple scales through time using remote sensing tools

Large-magnitude avalanches often alter vegetation composition, avalanche path dimensions, and subsequent avalanche return periods. Understanding temporal changes in individual avalanche path trimlines, runout zones, and geomorphic characteristics helps forecasters, planners, and engineers estimate potential avalanche destructive size and impact on infrastructure or settlements in the runout zone. Understanding these changes on a large scale also provides information on post-cycle avalanche distribution. Here, we use remote sensing platforms and change detection techniques to examine vegetation change in avalanche paths in Montana and Colorado. In northwest Montana, we implemented a novel approach using lidar, aerial imagery, and a random forest model to classify imagery-observed vegetation within avalanche paths. We calculated spatially explicit avalanche return periods using a physically based spatial interpolation method and characterized the vegetation within those return period zones. In Colorado, we investigated changes in avalanche path vegetation characteristics prior to and after a widespread large-magnitude avalanche cycle. The highest frequency of avalanche return periods was broadly characterized by grassland and shrubland, but topography greatly influences vegetation classes and return periods. Furthermore, statistically significant differences in lidar-derived vegetation canopy height exist between categorical return periods. We used optical sensors from satellite imagery to analyze changes in Normalized Difference Vegetation Index (NDVI) to calculate ground cover change over time. NDVI, a measure of near-infrared and red bands within the imagery, allowed us to distinguish between green vegetation (e.g., trees and shrubs) and non-vegetated ground cover (e.g., dead and downed trees, rocks, and dirt) within avalanche paths. For this study, we calculated changes in NDVI values by comparing imagery from 2018 to imagery from 2019 after a widespread large magnitude avalanche cycle occurred in March 2019 in Colorado, United States. We applied a filtering process to reduce error, classified NDVI change based on the value distribution, and then calculated area change of all areas within each avalanche path. We completed this process for 1633 avalanche paths throughout Colorado. We found that using NDVI difference values pre- and post-avalanche cycle allowed us to identify ground cover change in avalanche paths throughout Colorado. These changes span from a slight expansion of existing avalanche paths to substantial landscape disturbance. For example, a size D5 avalanche caused severe ground cover change in 18% of one single path near Aspen, Colorado. This suggests that large magnitude avalanches can redefine avalanche path dimensions and could impact subsequent avalanche size and frequency. Using NDVI from satellite imagery is a simple way to detect ground cover changes in avalanche paths on a large scale or in remote areas. In general, remote sensing products to detect and examine vegetation and ground cover change in avalanche paths can help inform avalanche distribution and benefit planning efforts.

Montana↗

USGS2024

A case study and comparison of mid-winter warming and solar driven wet slab avalanche cycles

Wet slab avalanches are poorly understood and often difficult to forecast. Yet, wet slab avalanches can be destructive and may become more common in a changing climate. As the onset of wet avalanches moves earlier in the winter season due to climate change, understanding snowpack and meteorological characteristics of wet slab avalanches will become increasingly important. In this study, we examined two recent late-January wet slab cycles triggered by warming and solar input in the Rocky Mountains of Idaho and Montana, United States. We posed two questions to help us understand these potentially increasingly frequent, mid-winter wet slab cycles: i) what are the weather and snowpack patterns of two mid-winter wet slab cycles and ii) how did these non-rain-on-snow, mid-winter warming events compare to the historical climate normal? In both locations, a short-lived ridge of high pressure strengthened over the region from late January to early February 2024. Temperatures rapidly increased to 6° C at middle and upper-elevation locations and remained above freezing for 48 to 72 hours. Wet slab avalanches (Montana: n=68, Idaho: n=11) released on a layer of facets resting on a crust formed in late December and slab depths averaged 40 to 150 cm. Avalanches primarily occurred on southeast, south, and southwest aspects at middle and upper elevations. Additionally, positive January monthly temperature trends exist in both locations from 1990 to 2020, and these two cycles from 2024 highlight impacts of midwinter warming events with unstable snowpack conditions. These cycles also highlight how a relatively uncommon mid-winter warming event with low radiative input can produce very large destructive wet slab avalanches. Understanding mid-winter warming events and associated wet slab avalanche cycles will help us prepare and forecast for potentially more common scenarios like this in the future.

Idaho, Montana↗

USGS2024

An analytical approach for simulating effects of avalanches on mountain goat population dynamics: Implications for management and conservation

Mountain environments with snow avalanche hazard cover about 6% of Earth’s land area and occur on all continents. Whereas human risks associated with avalanche hazard have been widely studied, little is known about how avalanche activity affects population dynamics in mountain wildlife. Globally, 32 species of mountain ungulates across 70 countries occupy avalanche-prone terrain. Avalanches comprise the leading cause of mortality in coastal Alaskan mountain goats (mean = 36%, range = 23 - 65%, depending on area), and disproportionately remove prime-aged individuals from populations. The implications of such rates and patterns of mortality on population growth rate are likely to be significant given the species’ low reproductive productivity, but further clarity is needed. To fill this knowledge gap, we developed a sex- and age-specific population modeling approach that integrates both reproduction and mortality to simulate the effects of avalanche-caused mortality on population growth rate across a range of empirically-observed states of avalanche-caused mortality (minimum, mean, maximum). Simulations were conducted to illustrate model functionality, and also provide insight about potential avalanche impacts on population demographic processes. For example, when severe avalanche years occur populations can experience significant additive mortality and declines (up to 15%). Due to low reproductive rates and slow life-history strategy of the species, such impacts can lead to long demographic recovery times (up to 11 years). From a species conservation perspective, such impacts are striking, and highlight the utility of employing a quantitative modeling approach to predict possible effects of avalanches on mountain ungulate population dynamics and viability. Our work explicitly builds upon recent findings about the importance of avalanches on mountain-adapted animal populations, and associated implications for the cultural and ecological communities that depend on them.

Alaska↗

USGS2023

Tree-ring derived avalanche frequency and climate associations in a high-latitude, maritime climate

Snow avalanches are a natural hazard in mountainous areas worldwide with severe impacts that include fatalities, damage to infrastructure, disruption to commerce, and landscape disturbance. Understanding long-term avalanche frequency patterns, and associated climate and weather influences, improves our understanding of how climate change may affect avalanche activity. We used dendrochronological techniques to evaluate the historical frequency of large magnitude avalanches (LMAs) in the high-latitude climate of southeast Alaska, United States. We collected 434 cross sections throughout six avalanche paths near Juneau, Alaska. This resulted in 2706 identified avalanche growth disturbances between 1720 and 2018, which allowed us to reconstruct 82 years with LMA activity across three sub-regions. By combining this tree-ring-derived avalanche data set with a suite of climate and atmospheric variables and applying a generalized linear model to fit a binomial regression, we found that February and March precipitation and the Oceanic Niño Index (ONI) were significant predictors of LMA activity in the study area. Specifically, LMA activity occurred during winters with substantial February and March precipitation and neutral or negative (cold) ONI values, while years not characterized by LMAs occur more frequently during warm winters (positive ONI values). Our examination of the climate-avalanche relationship in southeast Alaska sheds light on important climate variables and physical processes associated with LMA years. These results can be used to inform long-term infrastructure planning and avalanche mitigation operations in an urban area, such as Juneau, where critical infrastructure is subject to substantial avalanche hazard.

Alaska↗

USGS2023

Comparing snowpack meteorological inputs to support regional wet snow avalanche forecasting

Wet snow avalanches are predicted to increase in frequency with climate change and are often difficult to forecast. Improving our understanding of wet snow avalanche timing will help with current forecasting challenges. The onset of wet snow avalanching is closely tied to the temporal progression of liquid water flow through the seasonal snowpack. Measuring the flow of water through the snowpack in-situ is difficult due to the spatial variability of snow depth and structure. However, physical snowpack models can potentially simulate this process. The accuracy of snowpack models is heavily dependent upon the quality of the meteorological input data. A thorough investigation of model output differences using several different meteorological inputs for forecasting water movement and wet snow avalanches has not yet been thoroughly investigated. Here, we evaluate indicators of regional wet snow avalanches produced by the SNOWPACK model using different meteorological input. We compare the accuracy of SNOWPACK modeled outputs driven by two different numerical weather prediction (NWP) forecast models: the High-Resolution Deterministic Prediction System (HRDPS) and the North American Model (NAMnest). We leverage hourly automated weather station data, daily operational avalanche observations along the Going-to-the-Sun Road in Glacier National Park, Montana, United States, and in-situ snow stratigraphy and wetness profile observations to validate the SNOWPACK modeled outputs. This research is directly applicable to avalanche forecasting operations and future avalanche research as wet snow avalanche timing evolves due to climate change.

Montana↗

USGS2023

Temporal evolution of slab and weak layer properties during the transition from dry to wet snowpack conditions

Wet-snow slab avalanches are destructive and may become more prevalent in a warming climate. This type of avalanche remains challenging to forecast because the underlying processes leading to wet-snow slab avalanche release are poorly understood. In this study, we examine the temporal evolution of weak layer and slab liquid water content (LWC), critical cut length, and propagation saw test (PST) results during the season's first critical melt period at our study site in the Madison Mountains of southwest Montana. We used snowpack profiles and in-situ weather station data to initialize and force the 1-D physics-based snow cover model SNOWPACK throughout the winter and spring seasons. We then used a high-resolution numerical weather model to force SNOWPACK simulations to forecast the onset of the transition from dry to wet conditions. From April 10-12, 2023, we conducted 67 PSTs, 1053 LWC measurements, 20 hardness profiles, and a full snow profile each morning and early evening. During the first two days of sampling, we observed a transition from low to high propagation propensity with decreasing cut lengths and increasing LWC. On Day 3, we observed consistently low propagation propensity, even as LWC levels remained elevated and comparable to the preceding period of high propagation propensity. This indicates that there is a point where the relationship we observed through the first two days between increasing LWC, increasing propagation propensity, and decreasing cut length no longer holds. Our results further suggest PST propagation mode may help pinpoint the onset, peak, and decline of wet-snow fracture propagation propensity.

Montana↗

USGS2023

Mapping a glide avalanche with terrestrial lidar in Glacier National Park, USA

Thorough documentation of large avalanche events is important for forecasting efforts, infrastructure planning, and investigating the processes involved in avalanche formation and release. However, due in part to the isolated and dangerous nature of avalanche terrain, collecting in-situ, spatially continuous, and quantitative information surrounding avalanches remains difficult. Advances in remote sensing continue to address this knowledge gap. For example, terrestrial laser scanners (TLSs) can produce snow depth measurements at fine spatial resolutions over large areas. Repeat data acquisitions between precipitation events also allow for depth quantification atop an interface, as well as precise estimations of release volume and runout area after avalanche failure. Here, we explore the benefits of TLS-derived documentation from a large avalanche event by examining the development and release of a glide avalanche that occurred in Glacier National Park, Montana, USA, during the spring of 2022. Three sets of lidar point cloud data were acquired in the Haystack Creek drainage, focused on a well-known glide avalanche site. Lidar scans were collected after glide cracks emerged but prior to glide failure, and shortly (~ 1.5 days) after avalanche occurrence, in addition to a snow-free scan later in the year. With this temporal dataset, we were able to account for and visualize the spatial variability of snow depth across the avalanche start zone, such that we could precisely calculate the release volume (18674 m3) and average start zone depth (3.3 m) of the avalanche. Furthermore, TLS data were used to map the extent of the runout area and entrainment zone.

Montana↗

USGS2023

Spatial extent of forested avalanche terrain impacted by wildfire across the Sawtooth National Forest

Forest structure is a major driver of mountain snowpacks and avalanche occurrence. Healthy forests can reduce the incidence of dangerous slab avalanches, slow avalanches when in motion, shorten their runout distances, and act as a safety buffer for backcountry users, infrastructure, and transportation corridors. Since 1984, wildfire area in the seasonal snow zone of the western United States has increased by 70% throughout the seasonal snow zone, creating significant changes to avalanche prone mountains and their connected communities. A major unknown is the impact a reduction of forested area due to forest fires will have on avalanche occurrence. We hypothesize increased potential for avalanching in forested areas impacted by wildfire. Reduced tree cover may make previously heavily forested terrain more susceptible to avalanching. Increases in the size of avalanche start zones, paths, and runouts due to forest fires may increase the destructive size of avalanches and create cascading ecological effects within the adjacent forested terrain. Forest fires may therefore increase the likelihood of avalanche release, resulting in further loss of tree cover and increased avalanche area as well as decreased protection for human infrastructure. In this study, we quantify avalanche area changes before and after the Ross Fork wildfire (2022) in Sawtooth National Forest, Idaho, USA. We utilized satellite imagery, a digital elevation model and historical fire spatial data to quantify and characterize avalanche area changes within the fire perimeter using the Auto-ATES workflow (Sykes et al., 2022). We found decreases in forest coverage that contributed to widespread increases in potential avalanche release areas, avalanche tracks, and potential runout zones throughout the study area as well as the creation of new potential avalanche release areas and a substantial decrease in non-avalanche connected terrain within the fire perimeter. These preliminary findings help inform avalanche and snow safety professionals as well as land managers working in wildfire-prone forested areas about potential post-wildfire changes in avalanche terrain.

Idaho↗

USGS2023

The relationship between whumpf observations and avalanche activity in Colorado, USA

Triggering whumpfs is a primary indicator of unstable snowpack conditions. Although backcountry travelers and avalanche forecasters rely on whumpfs as a warning sign of potential avalanches, there is little formal research to confirm this relationship. This study investigated the temporal correlation between whumpfs and avalanche activity in data from Colorado's Front Range and southern San Juan Mountains between the winters of 2010/11 and 2022/23. To assess changing conditions over a variety of seasons, we compared the timing of whumpfs and avalanches to the total snow depth at a representative site. We used a 13-inch (33 cm) rolling-window average snow depth versus the median for observed whumpfs, and small avalanches (D1 to D1.5), and large to very large avalanches (D2 and greater). Our results support informal observations that whumpfs are important indicators of avalanche activity, especially at shallower snow depths. Later in the season, when snow depths are deeper and basal weak layers become more difficult to trigger, whumpfs become less common even during periods of increasing avalanche activity. Some of our results may be due to the thin, weak, and wind-affected snow in the Colorado Front Range, where whumpfing typically occurs due to collapsing basal depth hoar. Our findings are important for backcountry travelers assessing stability and for backcountry avalanche forecasters communicating conditions to the public. Our data show that although whumpfs generally indicate unstable conditions and correlate with avalanche activity, the largest avalanches of the winter may not always be preceded by whumpfing.

Colorado↗

USGS2023

Using tree rings to compare Colorado’s 2019 avalanche cycle to previous large avalanche cycles

Large magnitude avalanches (size ≥D3) impact settlements, transportation corridors, and public safety worldwide. In Colorado, United States, avalanches have killed more people than any other natural hazard since 1950. In March 2019, a historically large magnitude avalanche cycle occurred throughout the entire mountainous portion of Colorado resulting in more than 1000 reported avalanches during a 2-week period. Nearly 200 of these avalanches were size D4 or larger with at least three D5 avalanches. The extensive number of downed trees from this avalanche cycle allowed us to collect 1188 cross-sections and cores from 1023 unique trees within 24 avalanche paths across the state. We recorded 4135 growth disturbances in these samples. These data comprise the largest known avalanche tree-ring dataset in the world. We employed a strategic nested sampling design to account for scale by including several individual avalanche paths within a given drainage to create sub-regions and then sampled six major sub-regions (counties) throughout the greater region (state). We identified 76 avalanche years within 24 individual avalanche paths from 1698 to 2020. Large magnitude empirical avalanche event frequency varied across paths and sub-regions. Our results indicate the most widespread avalanche cycle in our study area prior to 2019 occurred in 1899, where 12 avalanche paths show evidence of large magnitude avalanche activity. Historical records also highlight 1899 as a year with widespread and large magnitude avalanche activity. These results indicate the avalanche cycle of March 2019 was of similar magnitude. Understanding the spatial extent and return frequency of large magnitude avalanche cycles across multiple spatial scales, from individual paths to an entire state, helps avalanche forecasters improve their products and mitigation strategies and assists infrastructure planners when designing and planning in avalanche terrain.

Colorado↗

USGS2023

Big avalanches in a changing climate: Using tree-ring derived avalanche chronologies to examine avalanche frequency across multiple climate types

Large-magnitude snow avalanches pose a hazard to humans and infrastructure worldwide. Analyzing the spatiotemporal behavior of avalanches and the contributory climate factors is important for understanding historical variability in climate-avalanche relationships as well as improving avalanche forecasting. This study uses established dendrochronological methods to develop long-term regional avalanche chronologies for three different climate types: high-latitude maritime climate of southeast Alaska, intermountain climate of the northern Rocky Mountains, and continental climate of Colorado. In the maritime study area, we collected 434 cross sections throughout six avalanche paths near Juneau, Alaska. This resulted in 2706 identified avalanche growth disturbances between year 1720 and 2018 Common Era (CE), which allowed us to reconstruct 82 years with large magnitude avalanche activity across three sub-regions. By combining this tree-ring derived avalanche dataset with a suite of climate and atmospheric variables and applying a generalized linear model to fit a binomial regression, we found February and March precipitation and the Oceanic Niño Index (ONI) were significant predictors of large magnitude avalanche activity in the southeast Alaska study area. In the intermountain climate study area, tree-rings from 647 trees exhibited 2134 avalanche-related growth disturbances in the northern Rocky Mountains of northwest Montana from 1867 to 2019. The data show that the amount of snowpack across the northern Rocky Mountain region is directly related to avalanche probability. Coincident with warming and regional snowpack reductions, a decline of ~ 14% (~ 2% per decade) in overall large magnitude avalanche probability is apparent through the period 1950–2017 CE. In the continental climate of Colorado, we sampled 24 avalanche paths throughout the state and collected 1188 total samples with 4135 identified growth disturbances from 1698 to 2019. Preliminary results suggest years with large magnitude avalanche activity across the sub-regions of this study area are generally characterized by stormy winters with above average snowpack development but that early and late winter temperature and precipitation also play an important role in large avalanche activity. Characterizing historical climate-avalanche relationships across different climate types provides a broad baseline for understanding potential future changes in avalanche activity. Overall, this work helps forecasters and planners better understand the influence of climate on large magnitude avalanche frequency, and how potential changes in avalanche character and occurrence will affect their operations in the context of a warming climate.

Alaska, Colorado, Montana↗

USGS2023

Under-forecasting wet avalanche cycles: Case studies and lessons learned from two wet avalanche cycles in northwest Montana and central Colorado

Predicting the timing and location of natural wet avalanche events is challenging, especially the release of wet slabs. In this study, we describe the existing snowpack structure, weather, and observed avalanche activity for two separate wet avalanche cycles in different avalanche climate types: northwest Montana and central Colorado. In both cases, the regional avalanche forecast centers initially predicted an avalanche hazard rating lower than the observed avalanche hazard and did not initially predict the occurrence of wet slab avalanches. Large wet slab avalanche activity began during the early stages of the first, rapid warming event of the season in both regions, challenging the notion that a lack of overnight refreeze is important for large wet avalanche cycles. This highlights the need for improvement in forecasting wet avalanche events/cycles. Here, we discuss lessons learned and operational strategies for improving forecast accuracy during significant melting events. These include focused field assessments in shallow snowpack areas and low elevation terrain, proactive late-day monitoring beyond normal field hours, and additional meteorological considerations, such as cloud cover and energy balance assessments, prior to and during notable warm-ups.

Colorado, Montana↗